Detailed Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
Q: We also consider the following baselines. BOW-Tags represents locations using a bag-of-words representation, using the same tag weighting as the embedding model. BOW-KL(Tags) uses the same representation but after term selection, using the same KL-based method as the embedding model. BOW-All combines the bag-of-words representation with the structured information, encoded as proposed in BIBREF7 . GloVe uses the objective from the original GloVe model for learning location vectors, i.e. this variant differs from EGEL-Tags in that instead of INLINEFORM1 we use the number of co-occurrences of tag INLINEFORM2 near location INLINEFORM3 , measured as INLINEFORM4 . 
 Question: what are the existing approaches?
A:
BOW-Tags BOW-KL(Tags) BOW-All GloVe